The present invention relates to systems and methods for controlling the presentation of learning materials to a student. More particularly, the invention relates to monitoring a cognitive state of the student during a learning session and using this information to set a level of difficulty of the content, range of the content, rate of delivery of the content or level of interactivity during the learning session in order to improve the student's learning of the material. Yet more particularly, the invention is used during the lecturing or scaffolding phases of learning where conventional student assessment methods would interrupt the cooperation between the student and a tutor. This invention also encompasses using the student's cognitive states, and information derived from them, to develop individualized learning models and perform long-term data tracking and analysis.
The embodiments described herein relate generally to tutoring, wherein a human- or computer-based teacher presents material to a student for purposes of learning the material. As used herein, “student” generally refers to any person who is being taught and can be of any age. Similarly, the material to be learned is not restricted in any way and can be any material presented to a child in a K-12 classroom or at home or to an adult in a classroom or on the job.
Human tutors make decisions regarding what learning content to present, what order in which to present this content and whether and when to repeat parts or all of a specific unit of content. In addition, a tutor can choose to give feedback and hints to guide or help a student. One hypothesis about human tutors is that they infer an accurate model of a student's competence and misunderstandings and use this diagnostic assessment to adapt their tutoring to the needs of the individual student. However, studies show that while human tutors generally know which material a student has mastered, they rarely know the student's misconceptions, erroneous beliefs and problematic skills. Further, studies have shown that even if human tutors accurately know a student's misconceptions and false beliefs, they do not seem to be able to effectively use this information to improve learning outcomes.
Assessments can be made to infer student mastery and correct beliefs. Usually these assessments are made in light of a student's response to a specific question or steps taken in solving a question. Accordingly, the learning content of a student will typically be interspersed with regular sets of testing or assessment. This testing has two consequences: a) it interrupts the flow of the learning material, and b) it slows the progress of the able students and potentially over-challenges and demotivates the less able students. Further, such an assessment must always be done after the learning period and thus, even if the assessment is correct, it must be updated retroactively.
Learning itself is typically comprised of four stages: evaluation, lecturing, scaffolding and assessment. Feedback from the tutor to the student can be applied throughout the learning session to enable the student to find flaws in their reasoning and knowledge. Further, scaffolding by its very nature involves guided prompts and hints to extend a student's own line of reasoning to increase their level of understanding. Thus, a key part of learning is for the tutor to decide when to offer guidance back to the student. However, doing so requires an accurate assessment of the state of the student.
Rather than attempt to project the particular state of an individual student throughout a learning session, adaptive teaching platforms aim to categorize a student's responses against a series of metrics based upon the cumulative performance of other students and then deliver the content based on these metrics. However, even if the student is perfectly categorized so that the optimum learning content can be delivered, day to day variability due to fatigue, emotional state and consumption of neuroactive agents such as coffee, alcohol or nicotine can render such categorization temporarily erroneous and affect the accuracy of a subsequent categorization.
A cognitive gauge can provide a near real-time quantification of the cognitive state of a human subject. Inputs to cognitive gauges range from a time interval between successive actions and responses by the subject to facial recognition methods that assess confusion, frustration and boredom to direct measurements of the physiological activity of the brain. Gauges for cognitive workload, engagement, fatigue and executive function have been developed. Cognitive gauges have mostly been used for the objective quantification of the effect of complex tasks on workers. Potential applications investigated to date have focused on the control of high value assets, such as aircraft and critical systems, and air traffic control management.
A significant potential value of cognitive gauges in teaching is that they can monitor aspects of the cognitive state of the student during, rather than merely upon completion of, the presentation of key learning content. However, while information has, in some instances, been gathered throughout a learning session, previously this information has only been used after a learning session is finished for use in adapting subsequent learning sessions. When used in this delayed mode, such cognitive information is only an adjunct to test results, self-reporting by the student and other measures of learning outcome. As a result, it has only incremental value over more established and cheaper methods.
Once a system with real-time, continuous adaptation of the material and methods using feedback based on a student's cognitive states is employed, this information can be used to create a new, or complement an existing, learning model for each student. Learning models are currently used by companies specializing in “big data” in an effort to understand how each student best learns to improve overall student learning outcomes. “Big data” is a term for a collection of data sets so large and complex that it is difficult to process using traditional data processing techniques. Some examples of big data inputs include the traditional adaptive learning platform metrics such as response accuracy, response time and the student's confidence in their response, but big data efforts are increasingly looking to other metrics that schools have access to, including student demographic data, census information, city and state records, etc. Most of these inputs, however, rely on the assumption that a specific student learns in a similar fashion as other students. Using cognitive state information and, specifically, how a student responds to different teaching methods guided by these cognitive state measurements, as an input into a learning model can further and more accurately personalize the teaching experience to each student to improve their learning outcomes.
What is needed is a system and method that can monitor in real-time the cognitive response of a student during the learning activity and can use this information to adapt and modify the content and delivery methods to improve the student's ability to learn, updating if desired a predictive model of the student's learning. Such a system and method would have immediate benefit to human tutors by reporting student's state during learning and suggesting specific changes to the teaching content and approach. For computerized tutors, it would enable individualized closed-loop control of teaching material and its presentation.